fishStan: Hierarchical Bayesian models for fisheries

Analysis of Hierarchical Bayesian Models for Large Space Time Data of the Housing Prices in Tehran

Housing price data is correlated to their location in different neighborhoods and their correlation is type of spatial (location). The price of housing is varius in different months, so they also have a time correlation. Spatio-temporal models are used to analyze this type of the data. An important purpose of reviewing this type of the data is to fit a suitable model for the spatial-temporal an...

Bayesian Hierarchical Models 1 Running head: BAYESIAN HIERARCHICAL MODELS Bayesian Hierarchical Models

Hierarchical Bayesian Models

Those of us who study human cognition have no easy task. We try to understand how people functionally represent and processes information in performing cognitive activities such as vision, perception, memory, language, and decision making. Fortunately, experimental psychology has a rich theoretical tradition, and there is no shortage of insightful theoretical proposals. Also, it has a rich expe...

Hierarchical Bayesian ARX models for robust inference

Gaussian innovations are the typical choice in most ARX models but using other distributions such as the Student’s t could be useful. We demonstrate that this choice of distribution for the innovations provides an increased robustness to data anomalies, such as outliers and missing observations. We consider these models in a Bayesian setting and perform inference using numerical procedures base...

Hierarchical Bayesian models for unsupervised scene understanding

For very large datasets with more than a few classes, producing ground-truth data can represent a substantial, and potentially expensive, human effort. This is particularly evident when the datasets have been collected for a particular purpose, e.g. scientific inquiry, or by autonomous agents in novel and inaccessible environments. In these situations there is scope for the use of unsupervised ...